Credit Risk Modeling with Graph Machine Learning

References

• Abbass P, Brownlees C, Hans C, Podlich N (2016) Credit risk interconnectedness: What does the market really know? J. Financial Stability 29:1–12.Google Scholar
• Ahmed A, Shervashidze N, Narayanamurthy S, Josifovski V, Smola AJ (2013) Distributed large-scale natural graph factorization. Proc. 22nd Internat. Conference on World Wide Web (ACM, New York), 37–48.Google Scholar
• Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23(4):589–609.Google Scholar
• Altman EI (2018) A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies. J. Credit Risk 14(4):1–34.Google Scholar
• Bae JW, Belo F, Li J, Lin X, Zhao X (2023) The opposing effects of complexity and information content on uncertainty dynamics: Evidence from 10-K filings. Management Sci. 69(10):5695–6415.Google Scholar
• Bai C, Shi B, Liu F, Sarkis J (2019) Banking credit worthiness: Evaluating the complex relationships. Omega 83(C):26–38.Google Scholar
• Ballen L (2021) Machine learning models and alternative data in credit scoring: Statistical and financial impact. MS Industrial Engineering thesis, Universidad de los Andes, Bogota, Columbia.Google Scholar
• Barabasi A-L, Bonabeau E (2003) Scale-free networks. Sci. Amer. 288(5):60–69.Google Scholar
• Benson AR, Gleich DF, Leskovec J (2016) Higher-order organization of complex networks. Science 353(6295):163–166.Google Scholar
• Billio M, Getmansky M, Lo AW, Pelizzon L (2012) Econometric measures of connectedness and systemic risk in the finance and insurance sectors. J. Financial Econom. 104(3):535–559.Google Scholar
• Black F, Scholes M (1973) The pricing of options and corporate liabilities. J. Political Econom. 81(3):637–654.Google Scholar
• Blumauer A, Nagy H (2020) The Knowledge Graph Cookbook (Monochrom, Vienna, Austria).Google Scholar
• Bonsall SB, Leone AJ, Miller BP, Rennekamp K (2017) A plain English measure of financial reporting readability. J. Accounting Econom. 63(2):329–357.Google Scholar
• Bracke P, Datta A, Jung C, Sen S (2019) Machine learning explainability in finance: An application to default risk analysis. Staff Working Paper No. 816, Bank of England.Google Scholar
• Brown S, Tucker JW (2011) Large-sample evidence on firms’ year-over-year MD&A modifications. J. Accounting Res. 49(2):309–346.Google Scholar
• Cai J, Eidam F, Saunders A, Steffen S (2018) Syndication, interconnectedness, and systemic risk. J. Financial Stability 34(C):105–120.Google Scholar
• Cao S, Lu W, Xu Q (2015) GraRep: Learning graph representations with global structural information. Proc. 24th ACM Internat. Conf. on Information and Knowledge Management (ACM, New York), 891–900.Google Scholar
• Cao S, Jiang W, Yang B, Zhang AL (2020) How to talk when a machine is listening: Corporate disclosure in the age of AI. Rev. Financial Stud. 36(9):3603–3642.Google Scholar
• Cavar D, Josefy M (2018) Mapping deep NLP to knowledge graphs: An enhanced approach to analyzing corporate filings with regulators. Proc. 11th Internat. Conf. on Language Resources and Evaluation (ELRA).Google Scholar
• Chan-Lau JA (2006) Fundamentals-based estimation of default probabilities: A survey. Working paper, International Monetary Fund, Washington, DC.Google Scholar
• Chen H, Perozzi B, Al-Rfou R, Skiena S (2018) A tutorial on network embeddings. Preprint, submitted August 8, https://doi.org/10.48550/arXiv.1808.02590.Google Scholar
• Cheng D, Niu Z, Zhang Y (2020) Contagious chain risk rating for networked-guarantee loans. Proc. 26th ACM SIGKDD Internat. Conf. on Knowledge Discovery & Data Mining (ACM, New York), 2715–2723.Google Scholar
• Ciano G, Rossi A, Bianchini M, Scarselli F (2022) On inductive–transductive learning with graph neural networks. IEEE Trans. Pattern Anal. Machine Intelligence 44(2):758–769.Google Scholar
• Cohen L, Malloy C, Nguyen Q (2020) Lazy prices. J. Finance 75(3):1371–1415.Google Scholar
• Das SR (2016) Matrix metrics: Network-based systemic risk scoring. J. Alternative Investment 18(4):33–51.Google Scholar
• Das SR, Hanouna P, Sarin A (2009) Accounting-based vs. market-based cross-sectional models of CDS spreads. J. Bank. Finance 33(4):719–730.Google Scholar
• Das SR, Kim S, Ostrov D (2019) Dynamic systemic risk networks. J. Financial Data Sci. 1:141–158.Google Scholar
• Das SR, Mitchener KJ, Vossmeyer A (2021) Bank regulation, network topology, and systemic risk: Evidence from the Great Depression. J. Money Credit Bank 54(5):1261–1312.Google Scholar
• Das SR, Donini M, Zafar MB, He J, Kenthapadi K (2022) FinLex: An effective use of word embeddings for financial lexicon generation. J. Finance Data Sci. 8:1–11.Google Scholar
• Desola V, Hanna K, Nonis P (2019) FinBERT: Pre-trained model on SEC filings for financial natural language tasks. Working paper, University of California, Berkeley.Google Scholar
• Duffie D, Singleton KJ (1999) Modeling term structures of defaultable bonds. Rev. Financial Stud. 12(4):687–720.Google Scholar
• Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (Curran Associates, Inc., San Jose, CA), 2224–2232.Google Scholar
• Dwyer D, Kocagil A, Stein R (2004) The Moody’s KMV RiskCalc v3. 1 Model: Next-Generation Technology for Predicting Private Firm Credit Risk (Moody’s KMV).Google Scholar
• Erickson N, Mueller J, Shirkov A, Zhang H, Larroy P, Li M, Smola A (2020) AutoGluon-Tabular: Robust and accurate AutoML for structured data. 7th ICML Workshop on Automated Machine Learning.Google Scholar
• Falkenstein EG, Boral A, Carty LV (2000) Riskcalc for private companies: Moody’s default model. Working paper, Moody’s Inc., New York.Google Scholar
• Fortunato S (2010) Community detection in graphs. Phys. Rep. 486(3):75–174.Google Scholar
• Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. Proc. 34th Internat. Conf. on Machine Learning (PMLR), 1263–1272.Google Scholar
• Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: A survey. Knowledge Base System 151:78–94.Google Scholar
• Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. Proc. 22nd ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (ACM, New York), 855–864.Google Scholar
• Gunning R (1952) The Technique of Clear Writing (McGraw-Hill, Toronto, Canada).Google Scholar
• Gupta S, Kumar P (2021) A constrained agglomerative clustering approach for unipartite and bipartite networks with application to credit networks. Inform. Sci. 557:332–354.Google Scholar
• Hamilton WL (2020) Graph Representation Learning (Morgan & Claypool, San Rafael, CA).Google Scholar
• Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. Proc. 31st Internat. Conf. on Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 1025–1035.Google Scholar
• Hamilton WL, Ying R, Leskovec J (2018) Inductive representation learning on large graphs. Preprint, submitted September 10, https://doi.org/10.48550/arXiv.1706.02216.Google Scholar
• Helwege J, Zhang G (2016) Financial firm bankruptcy and contagion. Rev. Finance 20(4):1321–1362.Google Scholar
• Hilscher J, Wilson M (2016) Credit ratings and credit risk: Is one measure enough? Management Sci. 63(10):3414–3437.Google Scholar
• Hutto C, Gilbert E (2014) VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proc. Internat. AAAI Conf. on Web and Social Media 8.Google Scholar
• Jarrow RA, Turnbull SM (1995) Pricing derivatives on financial securities subject to credit risk. J. Finance 50(1):53–85.Google Scholar
• Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. Proc. 5th Internat. Conf. on Learn. Representations (ICLR, Toulon, France).Google Scholar
• Kok C, Montagna M (2016) Multi-layered interbank model for assessing systemic risk. ECB Working Paper No. 1944, European Central Bank.Google Scholar
• Li F (2010) The information content of forward-looking statements in corporate filings–A naïve Bayesian machine learning approach. J. Accounting Res. 48(5):1049–1102.Google Scholar
• Li Y, Tarlow D, Brockschmidt M, Zemel RS (2016) Gated graph sequence neural networks. Bengio Y, LeCun Y, eds. Proc. 4th Internat. Conf. on Learn. Representations (ICLR, San Juan, Puerto Rico).Google Scholar
• Loughran T, Mcdonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66(1):35–65.Google Scholar
• Loughran T, McDonald B (2014) Measuring readability in financial disclosures. J. Finance 69(4):1643–1671.Google Scholar
• Ma Y, Tang J (2021) Deep Learning on Graphs (Cambridge University Press, Cambridge, UK).Google Scholar
• Merton RC (1974) On the pricing of corporate debt: The risk structure of interest rates. J. Finance 29(2):449–470.Google Scholar
• Muñoz-Cancino R, Bravo C, Ríos SA, Graña M (2021) On the combination of graph data for assessing thin-file borrowers’ creditworthiness. Preprint, submitted November 26, https://doi.org/10.48550/arXiv.2111.13666.Google Scholar
• Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J. Accounting Res. 18(1):109–131.Google Scholar
• Óskarsdóttir M, Bravo C (2021) Multilayer network analysis for improved credit risk prediction. Omega 105:102520.Google Scholar
• Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. Proc. 22nd ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (ACM, New York), 1105–1114.Google Scholar
• Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: Online learning of social representations. Proc. 20th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (ACM, New York), 701–710.Google Scholar
• Poenaru-Olaru L (2020) Credit scoring prediction using graph features. MS thesis, Delft University of Technology, Delft, Netherlands.Google Scholar
• Poledna S, Molina-Borboa JL, Martínez-Jaramillo S, van der Leij M, Thurner S (2015) The multi-layer network nature of systemic risk and its implications for the costs of financial crises. J. Financial Stability 20(C):70–81.Google Scholar
• Rios J, Zhao K, Street WN, Tian H, Zheng X (2020) A hybrid deep learning model for dynamic stock movement predictions based on supply chain networks. Workshop on Information Technology and Systems (WITS) (Social Science Research Network, Rochester, NY).Google Scholar
• Roukny T, Battiston S, Stiglitz JE (2018) Interconnectedness as a source of uncertainty in systemic risk. J. Financial Stability 35:93–106.Google Scholar
• Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans. Neural Networks 20(1):61–80.Google Scholar
• Shumovskaia V, Fedyanin K, Sukharev I, Berestnev D, Panov M (2021) Linking bank clients using graph neural networks powered by rich transactional data. Internat. J. Data Sci. Anal. 12(2):135–145.Google Scholar
• Sobehart JR, Stein R, Mikityanskaya V, Li L (2000) Moody’s public firm risk model: A hybrid approach to modeling short term default risk. Moody’s Investors Service, Global Credit Research, Rating Methodology, March.Google Scholar
• Surana A, Kumara S, Greaves M, Raghavan UN (2005) Supply chain networks: A complex adaptive systems perspective. Internat. J. Production Res. 43(20):4235–4265.Google Scholar
• Tobback E, Moeyersoms J, Stankova M, Martens D (2016) Bankruptcy prediction for SMEs using relational data. Working paper, University of Antwerp, Faculty of Business and Economics.Google Scholar
• Wang G-J, Jiang Z-Q, Lin M, Xie C, Stanley HE (2018) Interconnectedness and systemic risk of China’s financial institutions. Emerging Marketing Rev. 35:1–18.Google Scholar
• Wei Y, Yildirim P, Van den Bulte C, Dellarocas C (2016) Credit scoring with social network data. Marketing Sci. 35(2):234–258.Link, Google Scholar
• Ying Z, Bourgeois D, You J, Zitnik M, Leskovec J (2019) GNNExplainer: Generating explanations for graph neural networks. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates, Red Hook, NY), 9240–9251.Google Scholar
• Zaheer M, Guruganesh G, Dubey A, Ainslie J, Alberti C, Ontanon S, Pham P, et al. (2020) Big bird: Transformers for longer sequences. Advances in Neural Information Processing Systems, vol. 33, 17283–17297.Google Scholar
• Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, et al. (2020) Graph neural networks: A review of methods and applications. AI Open 1:57–81.Google Scholar